






"""simple docstring"""


import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__SCREAMING_SNAKE_CASE   =   get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')




@require_sentencepiece
@require_tokenizers
class 						__UpperCamelCase     (     UpperCamelCase   ,     unittest.TestCase						):
					lowercase_    :				Union[str, Any]      =  GPTSwaTokenizer
					lowercase_    :				Optional[int]      =  False
					lowercase_    :				List[Any]      =  True
					lowercase_    :				Dict      =  False
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       List[Any]:
											super().setUp()

											# We have a SentencePiece fixture for testing
											lowerCAmelCase   :int						=			GPTSwaTokenizer(UpperCAmelCase     , eos_token='<unk>'     , bos_token='<unk>'     , pad_token='<unk>'   )

											tokenizer.save_pretrained(self.tmpdirname   )
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	Any   )	->       Any:
											lowerCAmelCase   :Any						=			'This is a test'
											lowerCAmelCase   :List[str]						=			'This is a test'
											return input_text, output_text
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       List[Any]:
											lowerCAmelCase   :List[str]						=			'<s>'
											lowerCAmelCase   :List[Any]						=			1

											self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase   )     , UpperCAmelCase   )
											self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase   )     , UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Any   )	->       Any:
											lowerCAmelCase   :Union[str, Any]						=			list(self.get_tokenizer().get_vocab().keys()   )

											self.assertEqual(vocab_keys[0]     , '<unk>'   )
											self.assertEqual(vocab_keys[1]     , '<s>'   )
											self.assertEqual(vocab_keys[-1]     , 'j'   )
											self.assertEqual(len(UpperCAmelCase   )     , 2000   )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Optional[int]:
											self.assertEqual(self.get_tokenizer().vocab_size     , 2000   )
					def        UpperCAmelCase__ (							self							:	Optional[Any]   )	->       List[str]:
											lowerCAmelCase   :Union[str, Any]						=			GPTSwaTokenizer(UpperCAmelCase   )

											lowerCAmelCase   :List[Any]						=			tokenizer.tokenize('This is a test'   )
											self.assertListEqual(UpperCAmelCase     , ['▁This', '▁is', '▁a', '▁t', 'est']   )

											self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase   )     , [465, 287, 265, 631, 842]   )

											lowerCAmelCase   :str						=			tokenizer.tokenize('I was born in 92000, and this is falsé.'   )
											# fmt: off
											self.assertListEqual(
											    UpperCAmelCase     , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']     , )
											# fmt: on

											lowerCAmelCase   :int						=			tokenizer.convert_tokens_to_ids(UpperCAmelCase   )
											self.assertListEqual(
											    UpperCAmelCase     , [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]     , )

											lowerCAmelCase   :List[Any]						=			tokenizer.convert_ids_to_tokens(UpperCAmelCase   )
											# fmt: off
											self.assertListEqual(
											    UpperCAmelCase     , ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']   )
											# fmt: on
					def        UpperCAmelCase__ (							self							:	Optional[int]   )	->       Optional[int]:
											lowerCAmelCase   :str						=			GPTSwaTokenizer(UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			['This is a test', 'I was born in 92000, and this is falsé.']
											lowerCAmelCase   :Optional[int]						=			[
											    [465, 287, 265, 631, 842],
											    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
											]

											# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
											for text, expected_ids in zip(UpperCAmelCase     , UpperCAmelCase   ):
																	self.assertListEqual(tokenizer.encode_fast(UpperCAmelCase   )     , UpperCAmelCase   )

											# Test that decode_fast returns the input text
											for text, token_ids in zip(UpperCAmelCase     , UpperCAmelCase   ):
																	self.assertEqual(tokenizer.decode_fast(UpperCAmelCase   )     , UpperCAmelCase   )
					@slow
					def        UpperCAmelCase__ (							self							:	Tuple   )	->       Union[str, Any]:
											lowerCAmelCase   :List[Any]						=			[
											    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
											    'Hey there, how are you doing this fine day?',
											    'This is a text with a trailing spaces followed by a dot     .',
											    'Häj sväjs lillebrör! =)',
											    'Det är inget fel på Mr. Cool',
											]

											# fmt: off
											lowerCAmelCase   :str						=			{'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
											# fmt: on
											self.tokenizer_integration_test_util(
											    expected_encoding=UpperCAmelCase     , model_name='AI-Sweden/gpt-sw3-126m'     , sequences=UpperCAmelCase     , )







"""simple docstring"""


from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						for param, grad_param in zip(model_a.parameters()     ,      model_b.parameters() ):
												if not param.requires_grad:
																		continue
												if not did_step:
																		# Grads should not be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is False
																		), F"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
												else:
																		# Grads should be in sync
																		assert (
																		    torch.allclose(param.grad     ,      grad_param.grad ) is True
																		), F"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__=True ):





						'''simple docstring'''






						model.train()
						lowerCAmelCase   :Dict						=			model(a__ )
						lowerCAmelCase   :Union[str, Any]						=			F.mse_loss(a__     ,      target.to(output.device ) )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(a__ )


def        UpperCAmelCase      (					a__     ,      a__=False ):





						'''simple docstring'''






						set_seed(42 )
						lowerCAmelCase   :Any						=			RegressionModel()
						lowerCAmelCase   :List[str]						=			deepcopy(a__ )
						lowerCAmelCase   :str						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						model.to(accelerator.device )
						if sched:
												lowerCAmelCase   :List[Any]						=			AdamW(params=model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Dict						=			AdamW(params=ddp_model.parameters()     ,      lr=1e-3 )
												lowerCAmelCase   :Tuple						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
												lowerCAmelCase   :Optional[Any]						=			LambdaLR(a__     ,      lr_lambda=lambda a__ : epoch**0.65 )
						# Make a copy of `model`
						if sched:
												lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :int						=			accelerator.prepare(a__     ,      a__     ,      a__     ,      a__ )
						else:
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			accelerator.prepare(a__     ,      a__ )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Any						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :Any						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		assert torch.allclose(
																		    param.grad     ,      ddp_param.grad ), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Any						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Dict						=			get_training_setup(a__ )
						# Use a single batch
						lowerCAmelCase	, lowerCAmelCase   :str						=			next(iter(a__ ) ).values()
						for iteration in range(3 ):
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :List[Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :int						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(a__ ):
																								step_model(a__     ,      a__     ,      a__     ,      a__ )
												else:
																		# Sync grads
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if iteration % 2 == 0:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :str						=			ddp_input[torch.randperm(len(a__ ) )]


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :str						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			get_training_setup(a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Dict						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Any						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												# Do "gradient accumulation" (noop)
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()     ,      ddp_model.parameters() ):
																		if not param.requires_grad:
																								continue
																		if ((iteration + 1) % 2 == 0) or (iteration == len(a__ ) - 1):
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is True
																								), F"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
																		else:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad     ,      ddp_param.grad ) is False
																								), F"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
												lowerCAmelCase   :Optional[int]						=			ddp_input[torch.randperm(len(a__ ) )]
						GradientState._reset_state()


def        UpperCAmelCase      (					a__=False     ,      a__=False ):





						'''simple docstring'''






						lowerCAmelCase   :Optional[int]						=			Accelerator(
						    split_batches=a__     ,      dispatch_batches=a__     ,      gradient_accumulation_steps=2 )
						# Test that context manager behaves properly
						lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			get_training_setup(a__     ,      a__ )
						for iteration, batch in enumerate(a__ ):
												lowerCAmelCase	, lowerCAmelCase   :Optional[int]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												lowerCAmelCase	, lowerCAmelCase   :Union[str, Any]						=			accelerator.gather((ddp_input, ddp_target) )
												lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			input.to(accelerator.device ), target.to(accelerator.device )
												# Perform our initial ground truth step in non "DDP"
												model.train()
												ddp_model.train()
												step_model(a__     ,      a__     ,      a__     ,      a__     ,      a__ )
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(a__ )):
																		if split_batches:
																								sched.step()
																		else:
																								for _ in range(accelerator.num_processes ):
																														sched.step()
												opt.zero_grad()
												# Perform gradient accumulation under wrapper
												with accelerator.accumulate(a__ ):
																		step_model(a__     ,      a__     ,      a__     ,      a__ )
																		ddp_opt.step()
																		ddp_sched.step()
																		ddp_opt.zero_grad()

												# Learning rates should be the same
												assert (
												    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
												), F"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
												lowerCAmelCase   :int						=			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(a__ ))
												if accelerator.num_processes > 1:
																		check_model_parameters(a__     ,      a__     ,      a__     ,      a__ )
												# Shuffle ddp_input on each iteration
												torch.manual_seed(13_37 + iteration )
						GradientState._reset_state()


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :int						=			Accelerator()

						lowerCAmelCase   :int						=			RegressionDataset(length=80 )
						lowerCAmelCase   :Optional[Any]						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase   :Any						=			RegressionDataset(length=96 )
						lowerCAmelCase   :Dict						=			DataLoader(a__     ,      batch_size=16 )
						lowerCAmelCase	, lowerCAmelCase   :Tuple						=			accelerator.prepare(a__     ,      a__ )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(a__ ):
												assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
												if iteration < len(a__ ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(a__ ):
																														assert id(accelerator.gradient_state.active_dataloader ) == id(a__ )
																														if batch_num < len(a__ ) - 1:
																																				assert not accelerator.gradient_state.end_of_dataloader
																														else:
																																				assert accelerator.gradient_state.end_of_dataloader
												else:
																		assert accelerator.gradient_state.end_of_dataloader
						assert accelerator.gradient_state.active_dataloader is None


def        UpperCAmelCase      (					):





						'''simple docstring'''






						lowerCAmelCase   :List[str]						=			Accelerator()
						lowerCAmelCase   :Optional[int]						=			accelerator.state
						if state.local_process_index == 0:
												print('**Test `accumulate` gradient accumulation with dataloader break**' )
						test_dataloader_break()
						if state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print('**Test NOOP `no_sync` context manager**' )
												test_noop_sync(a__ )
						if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
												if state.local_process_index == 0:
																		print('**Test Distributed `no_sync` context manager**' )
												test_distributed_sync(a__ )
						if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																		for dispatch_batches in [True, False]:
																								if state.local_process_index == 0:
																														print(
																														    '**Test `accumulate` gradient accumulation, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																								test_gradient_accumulation(a__     ,      a__ )

    # Currently will break on torch 2.0 +, need to investigate why
						if is_torch_version('<'     ,      '2.0' ) or state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print(
																		    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      '`split_batches=False`, `dispatch_batches=False`**'     ,      )
												test_gradient_accumulation_with_opt_and_scheduler()
												if state.distributed_type == DistributedType.MULTI_GPU:
																		for split_batch in [True, False]:
																								for dispatch_batches in [True, False]:
																														if not split_batch and not dispatch_batches:
																																				continue
																														if state.local_process_index == 0:
																																				print(
																																				    '**Test `accumulate` gradient accumulation with optimizer and scheduler, '     ,      F"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**"""     ,      )
																														test_gradient_accumulation_with_opt_and_scheduler(a__     ,      a__ )


def        UpperCAmelCase      (					a__ ):





						'''simple docstring'''






						main()


if __name__ == "__main__":
		main()